Classification of Processing Damage in Sugar Beet (<i>Beta vulgaris</i>) Seeds by Multispectral Image Analysis
The pericarp of monogerm sugar beet seed is rubbed off during processing in order to produce uniformly sized seeds ready for pelleting. This process can lead to mechanical damage, which may cause quality deterioration of the processed seeds. Identification of the mechanical damage and classification...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/10/2360 |
| _version_ | 1811279079273398272 |
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| author | Zahra Salimi Birte Boelt |
| author_facet | Zahra Salimi Birte Boelt |
| author_sort | Zahra Salimi |
| collection | DOAJ |
| description | The pericarp of monogerm sugar beet seed is rubbed off during processing in order to produce uniformly sized seeds ready for pelleting. This process can lead to mechanical damage, which may cause quality deterioration of the processed seeds. Identification of the mechanical damage and classification of the severity of the injury is important and currently time consuming, as visual inspections by trained analysts are used. This study aimed to find alternative seed quality assessment methods by evaluating a machine vision technique for the classification of five damage types in monogerm sugar beet seeds. Multispectral imaging (MSI) was employed using the VideometerLab3 instrument and instrument software. Statistical analysis of MSI-derived data produced a model, which had an average of 82% accuracy in classification of 200 seeds in the five damage classes. The first class contained seeds with the potential to produce good seedlings and the model was designed to put more limitations on seeds to be classified in this group. The classification accuracy of class one to five was 59, 100, 77, 77 and 89%, respectively. Based on the results we conclude that MSI-based classification of mechanical damage in sugar beet seeds is a potential tool for future seed quality assessment. |
| first_indexed | 2024-04-13T00:48:19Z |
| format | Article |
| id | doaj.art-0127742d673c45d5a364469df2ab3e71 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T00:48:19Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0127742d673c45d5a364469df2ab3e712022-12-22T03:09:56ZengMDPI AGSensors1424-82202019-05-011910236010.3390/s19102360s19102360Classification of Processing Damage in Sugar Beet (<i>Beta vulgaris</i>) Seeds by Multispectral Image AnalysisZahra Salimi0Birte Boelt1Department of Agroecology, Aarhus University, 4200 Slagelse, DenmarkDepartment of Agroecology, Aarhus University, 4200 Slagelse, DenmarkThe pericarp of monogerm sugar beet seed is rubbed off during processing in order to produce uniformly sized seeds ready for pelleting. This process can lead to mechanical damage, which may cause quality deterioration of the processed seeds. Identification of the mechanical damage and classification of the severity of the injury is important and currently time consuming, as visual inspections by trained analysts are used. This study aimed to find alternative seed quality assessment methods by evaluating a machine vision technique for the classification of five damage types in monogerm sugar beet seeds. Multispectral imaging (MSI) was employed using the VideometerLab3 instrument and instrument software. Statistical analysis of MSI-derived data produced a model, which had an average of 82% accuracy in classification of 200 seeds in the five damage classes. The first class contained seeds with the potential to produce good seedlings and the model was designed to put more limitations on seeds to be classified in this group. The classification accuracy of class one to five was 59, 100, 77, 77 and 89%, respectively. Based on the results we conclude that MSI-based classification of mechanical damage in sugar beet seeds is a potential tool for future seed quality assessment.https://www.mdpi.com/1424-8220/19/10/2360machine visionmechanical damageprediction modelseed qualityseed polishing |
| spellingShingle | Zahra Salimi Birte Boelt Classification of Processing Damage in Sugar Beet (<i>Beta vulgaris</i>) Seeds by Multispectral Image Analysis Sensors machine vision mechanical damage prediction model seed quality seed polishing |
| title | Classification of Processing Damage in Sugar Beet (<i>Beta vulgaris</i>) Seeds by Multispectral Image Analysis |
| title_full | Classification of Processing Damage in Sugar Beet (<i>Beta vulgaris</i>) Seeds by Multispectral Image Analysis |
| title_fullStr | Classification of Processing Damage in Sugar Beet (<i>Beta vulgaris</i>) Seeds by Multispectral Image Analysis |
| title_full_unstemmed | Classification of Processing Damage in Sugar Beet (<i>Beta vulgaris</i>) Seeds by Multispectral Image Analysis |
| title_short | Classification of Processing Damage in Sugar Beet (<i>Beta vulgaris</i>) Seeds by Multispectral Image Analysis |
| title_sort | classification of processing damage in sugar beet i beta vulgaris i seeds by multispectral image analysis |
| topic | machine vision mechanical damage prediction model seed quality seed polishing |
| url | https://www.mdpi.com/1424-8220/19/10/2360 |
| work_keys_str_mv | AT zahrasalimi classificationofprocessingdamageinsugarbeetibetavulgarisiseedsbymultispectralimageanalysis AT birteboelt classificationofprocessingdamageinsugarbeetibetavulgarisiseedsbymultispectralimageanalysis |